Yearly Traffic Safety Analysis

183 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Wright County, total crashes increased from 140 in 2022 to 183 in 2023, a rise of 30.7%. While total fatalities remained constant at 3, the number of crashes resulting in serious injuries increased significantly, from 2 in the prior period to 13 in the current period.

183

▲ 30.7%was 140

Total Crash Events

3

Persons Killed

57

▲ 23.9%was 46

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Wright County indicates a rising trend year-over-year. Total collisions increased by 30.7%, from 140 in 2022 to 183 in 2023. This increase was accompanied by a 23.9% rise in total injuries, from 46 to 57, while total fatalities remained unchanged at 3 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 1→0.0%

55

Motorists Injured

Prior: 45↑22.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Wright County shifted between the two periods. In 2023, the peak day for crashes was Wednesday with 31 incidents, a change from Thursday (28 incidents) in 2022. The peak hour also shifted earlier in the day, from 5 p.m. in the prior year (15 crashes) to 3 p.m. in the current year (19 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes in Wright County worsened in 2023 compared to the previous year. The number of fatal crashes doubled from 1 to 2, and the fatal crash rate increased from 0.71 to 1.09 per 100 crashes. Crashes resulting in serious injuries saw a substantial increase, rising from 2 incidents (1.4% of total) in 2022 to 13 incidents (7.1% of total) in 2023. The overall proportion of crashes resulting in any injury or fatality remained stable at 25.7% in both periods.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
↑100.0%prior 1
Serious Injury13serious injury crashes7.1%
↑550.0%prior 2
Minor Injury19minor injury crashes10.4%
↑18.8%prior 16
Possible Injury13possible injury crashes7.1%
↓-23.5%prior 17
No Injury136no injury crashes74.3%
↑30.8%prior 104

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the leading contributing factor in Wright County, with the count increasing from 29 in 2022 to 41 in 2023. Several other top factors also saw an increase in frequency; 'Lost Control' incidents rose from 7 to 13, and crashes where a vehicle 'Ran off road - left' increased from 8 to 14. 'Driving too fast for conditions' became a more prominent factor, with the number of related crashes growing from 6 to 10.

Officer-Reported Primary Contributing Cause

Animal41 (22.4%)↑41.4%prior 29
Ran off road - left14 (7.7%)↑75.0%prior 8
Lost Control13 (7.1%)↑85.7%prior 7
Other (explain in narrative): Other12 (6.6%)↓-14.3%prior 14
Driving too fast for conditions10 (5.5%)↑66.7%prior 6
Ran off road - straight10 (5.5%)↑66.7%prior 6
Followed too close7 (3.8%)
Driver Distraction: Other interior distraction6 (3.3%)
Made improper turn5 (2.7%)
Improper Backing5 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across environmental conditions showed some year-over-year changes. While the proportion of crashes in clear weather remained steady at approximately 56%, there was a notable shift in lighting conditions. The share of crashes occurring in daylight decreased from 60.0% in 2022 to 49.7% in 2023, while crashes in dark, unlighted conditions increased from 11 to 29 incidents, representing a rise from 7.9% to 15.8% of all crashes. Crashes on dry road surfaces also became more common, increasing from 47.1% to 56.8% of the total.

Weather

Clear103 (71.5%)
↑30.4%prior 79
Cloudy23 (16.0%)
↑109.1%prior 11
Snow8 (5.6%)
↑33.3%prior 6
Freezing rain/drizzle5 (3.5%)
Rain3 (2.1%)
Fog, smoke, smog1 (0.7%)
Blowing Snow1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight91 (62.8%)
↑8.3%prior 84
Dark - roadway not lighted29 (20.0%)
↑163.6%prior 11
Dark - roadway lighted13 (9.0%)
↑62.5%prior 8
Dusk8 (5.5%)
Dawn4 (2.8%)
↓-33.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry104 (71.7%)
↑57.6%prior 66
Ice/frost17 (11.7%)
↑41.7%prior 12
Gravel8 (5.5%)
↓-20.0%prior 10
Wet6 (4.1%)
↓-25.0%prior 8
Snow5 (3.4%)
↓-61.5%prior 13
Slush2 (1.4%)
Mud, dirt2 (1.4%)
Other (explain in narrative)1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

An analysis of vehicles involved in crashes shows a shift in the most common makes. In 2023, Chevrolet vehicles (72) were more frequently involved than Ford vehicles (43), reversing the trend from 2022 when Ford (44) was the top make ahead of Chevrolet (53). Regarding the age of persons involved, the 35-44 age group remained the most represented, increasing from 57 to 77 individuals. Notably, the involvement of the 16-20 age group grew from 35 to 58 people, while the 65+ age group's involvement decreased from 45 to 27 people.

Top Vehicle Makes (269 vehicles)

1
CHEV57 (21.2%)
↑46.2%prior 39
2
FORD43 (16%)
↓-2.3%prior 44
3
CHEVROLET15 (5.6%)
↑7.1%prior 14
4
NISS14 (5.2%)
↑180.0%prior 5
5
FREIGHTLINER12 (4.5%)
6
DODG11 (4.1%)
↑37.5%prior 8
7
TOYO8 (3%)
↑60.0%prior 5
8
TOYT7 (2.6%)
↑16.7%prior 6
9
GMC7 (2.6%)
↓-61.1%prior 18
10
JEEP7 (2.6%)
↓-41.7%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

48 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (237 persons with recorded sex)

Male156 (65.8%)
↑24.8%prior 125
Female81 (34.2%)
↑2.5%prior 79

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 183
  • Total persons involved: 393
  • Total vehicles involved: 269

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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